CN112883577B - Method for generating typical scene of output of offshore wind farm and storage medium - Google Patents
Method for generating typical scene of output of offshore wind farm and storage medium Download PDFInfo
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Abstract
The invention discloses a method for generating an output typical scene of an offshore wind farm and a storage medium, wherein the method comprises the steps of collecting output data of the offshore wind farm and marine meteorological data, detecting and processing the output data of the offshore wind farm and the marine meteorological data, and obtaining new output data of the offshore wind farm and marine meteorological data; dividing new offshore wind farm output data and offshore meteorological data into S seasons according to ocean monsoon characteristics, and obtaining k weather characteristic indexes of the S-th season output of the offshore wind farm
Wherein the weather features include ocean non-extreme weather and ocean extreme weather; according to characteristic indexes of ocean non-extreme weather
Determining a typical scene of the output of the offshore wind farm in the s Ji Haiyang non-extreme weather and probability thereof; according to characteristic index of extreme days
And determining a typical scene of the output of the offshore wind farm in the extreme weather of the s < th > season ocean and probability thereof. According to the invention, through correlation analysis, weather characteristic indexes affecting the output of the offshore wind farm are accurately identified, and the accuracy and the representativeness of the selected scene are improved.
Description
Technical Field
The invention relates to the technical field of offshore wind power generation, in particular to a method for generating an output typical scene of an offshore wind farm and a storage medium.
Background
The coastline of China is long, the offshore wind energy resources are rich, and the method has great development potential. Compared with land wind resources, the offshore wind energy resources have a plurality of advantages, and the indexes such as average wind speed, effective wind energy density, wind power level, available effective wind speed hours, turbulence intensity and the like are all superior to the land wind resources. However, the characteristics of the ocean monsoon of the coastal wind energy resources are quite obvious due to the influence of the geographic position, for example, wind in winter and summer can show a certain specific wind direction, coastal weather disasters frequently occur, and tropical cyclones such as tropical storms, typhoons and the like can bring about the influence of both advantages and disadvantages to offshore wind power generation. The offshore wind power output has the following typical characteristics under the influence of the endowment of offshore wind energy resources: the fluctuation of the offshore wind power output is lower; the degree and probability of occurrence of the anti-peak shaving are stronger than those of land wind power; the output is closely related to the marine weather; the output has obvious seasonal characteristics and the like. Various characteristics of offshore wind power bring great challenges to production simulation analysis of a power system, and building an output model capable of accurately reflecting the output characteristics of the offshore wind power is a precondition for realizing large-scale offshore wind power optimization and power system planning work.
The conventional land wind power output modeling method is a typical scene method, can ensure the original characteristics of output data, has the model calculation efficiency, and is widely applied to the research of an electric power system based on scene analysis. Besides the method, a probability statistical method based on wind power time sequence output data and a wind power output modeling method based on a multi-state machine set method are provided.
However, the current wind power plant output modeling method is mainly aimed at land wind power plant characteristics and is established according to the mapping relation between land wind speed and fan output, but as the offshore wind power has larger difference from the land wind power plant in aspects of wind speed characteristics, wind power plant arrangement and the like, the offshore wind power has more complex and extreme output scenes due to stronger anti-peak regulation characteristics and changeable marine climate characteristics, and the output scenes are important for the medium-and-long-term development planning research of a large-scale offshore wind power system, the existing method cannot adapt to the output characteristics of the offshore wind power plant, so that the acquired offshore wind power data is inaccurate, and the accuracy of modeling calculation in the typical output scene of the offshore wind power plant is affected.
Disclosure of Invention
The invention aims to provide a method for generating a typical scene of the output of an offshore wind farm and a storage medium, and weather characteristic indexes influencing the output of the offshore wind farm are accurately identified through correlation analysis, so that the accuracy and the representativeness of the selected scene are improved.
In order to achieve the above object, an embodiment of the present invention provides a method for generating an output typical scenario of an offshore wind farm, including:
acquiring, detecting and processing output data of an offshore wind farm and offshore meteorological data to acquire new output data of the offshore wind farm and offshore meteorological data;
dividing the new output data of the offshore wind farm and the new meteorological data into the S season according to the characteristics of the ocean monsoon, screening the new output data of the offshore wind farm and the new meteorological data, and obtaining k weather characteristic indexes of the S-th season output of the offshore wind farm
Wherein the weather characteristics include ocean non-extreme weather and ocean extreme weather;
according to the ocean non-poleCharacteristic index of end weather
Determining a typical scene of the output of the offshore wind farm in the s Ji Haiyang non-extreme weather and probability thereof;
according to the characteristic index of the extreme sky
And determining a typical scene of the output of the offshore wind farm in the extreme weather of the s < th > season ocean and probability thereof.
Preferably, the acquiring the output data of the offshore wind farm and the offshore meteorological data for detection and processing, and acquiring new output data of the offshore wind farm and offshore meteorological data, includes:
the detection and processing comprises missing data correction, abnormal data correction and ocean extreme weather screening;
the missing data correction comprises the step of correcting data by adopting linear interpolation if the number of missing data does not exceed the limit value allowed by errors;
the abnormal data correction comprises the steps of verifying data exceeding a data limit value or short-time mutation, deleting the data exceeding the data limit value or short-time mutation and correcting missing data;
the marine extreme weather screening comprises the steps of screening out the output data of the offshore wind farm under the marine extreme weather according to the influence of the marine extreme weather on the output of the offshore wind farm and analyzing the output data independently.
Preferably, the new output data of the offshore wind farm and the new weather data of the offshore wind farm are divided into the S seasons according to the characteristics of ocean monsoon, the new output data of the offshore wind farm and the new weather data of the offshore wind farm are screened, and k weather characteristic indexes of the S th season output of the offshore wind farm are obtained
Wherein the weather features include ocean non-extreme weather and ocean extreme weather, including:
according to the new output number of the offshore wind farmConstructing a multiple regression analysis equation according to climate data, wherein the Pearson correlation coefficient R of the output x of the s-th offshore wind farm and the climate data y xy The following are provided:
wherein,,
and->
Is the sample mean of the output data x and the climate factor data y.
Preferably, the new output data of the offshore wind farm and the offshore meteorological data are divided into the S seasons according to the characteristics of ocean monsoon, the new output data of the offshore wind farm and the new offshore meteorological data are screened, and weather characteristic indexes of the S th season output of the offshore wind farm are obtained
Wherein the weather features include ocean non-extreme weather and ocean extreme weather, including:
if the correlation coefficient R xy Positive, the method is used for representing that the output data x of the offshore wind farm and the climate data y are positively correlated;
if the correlation coefficient R xy Negative, for representing that the output data x and the climate data y of the offshore wind farm are in negative correlation;
wherein the absolute value of the correlation coefficient tends to be 1, which indicates that the correlation between the offshore wind farm output data x and the climate data y is stronger, and the absolute value of the correlation coefficient tends to be 0, which indicates that the correlation between the offshore wind farm output data x and the climate data y is weaker.
Preferably, the step of dividing the new output data of the offshore wind farm and the offshore meteorological data into the S season according to the characteristics of ocean monsoon, and the step of screening the new output data of the offshore wind farm and the new offshore meteorological data to obtain weather characteristics of the S th season output of the offshore wind farm refers toLabel (C)
Wherein the weather features include ocean non-extreme weather and ocean extreme weather, including:
according to the correlation coefficient R xy >Weather data y of 0.4 are obtained, and weather characteristic indexes of the s-th season output of the offshore wind farm are obtained
Preferably, the characteristic index according to the ocean non-extreme weather
Determining a typical scenario of offshore wind farm output and probability thereof in the s Ji Haiyang non-extreme weather, including:
the characteristic indexes of the ocean non-extreme weather comprise that the daily average output is
Peak daily load output->
Setting the daily peak load output
Confidence level alpha E (0, 1), and screening out that the peak output of the load on the s th season is not less than +.>
The probability of (a) is greater than alpha, the daily output curve of the offshore wind farm +.>
The following are provided:
according to the daily output curve of the offshore wind farm
Screening out average output +.>
Not less than the daily output curve of the offshore wind farm +.>
Confidence level epsilon (0, 1) of the solar output curve P of the offshore wind farm ε s The following are provided:
preferably, the characteristic index according to the ocean non-extreme weather
Determining a typical scenario of offshore wind farm output and probability thereof in the s Ji Haiyang non-extreme weather, including:
according to the daily output curve P of the offshore wind farm ε s Determining typical output scene of offshore wind farm in ocean non-extreme weather W under s-th season confidence level alpha
The following are provided:
the probability of the typical scene of the output of the offshore wind farm is calculated as follows:
wherein,,
for the probability of occurrence of the typical scenario of output under weather W and confidence level alpha, N ε P in weather W ε s Total number of medium day output curves.
Preferably, the characteristic index according to the ocean extreme weather
Determining a typical scene of the output of the offshore wind farm in the s-th ocean extreme weather and probability thereof, wherein the method comprises the following steps of:
the characteristic indexes of the ocean extreme weather comprise that the average output is daily
Peak daily load output->
Setting the daily peak load output
Confidence level beta E (0, 1), and screening out that the peak output of the load on the s th season is not less than +.>
Is greater than beta, the daily output curve of the offshore wind farm +.>
The following are provided:
preferably, the characteristic index according to the ocean extreme weather
Determining offshore in extreme weather of the s-th oceanWind farm output typical scenarios and their probabilities, including:
typical scenario of output of offshore wind farm in ocean extreme weather W' under s-th season confidence level beta
The following are provided:
the probability of the typical scene of the output of the offshore wind farm is calculated as follows:
wherein,,
is the occurrence probability of the typical output scene under the weather W' and the confidence level beta, N β For weather W->
Total number of medium day output curves.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which is characterized in that the computer program, when being executed by a processor, realizes the method for generating the typical scene of the output of the offshore wind farm according to any embodiment.
According to the method for generating the typical scene of the output of the offshore wind farm, provided by the embodiment of the invention, the output data of the offshore wind farm and the marine meteorological data are acquired, detected and processed, the data after detection and processing are classified into s seasons, and weather characteristic indexes influencing the output of the offshore wind farm are accurately identified through correlation analysis, so that the accuracy and the typical performance of the selected scene are improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for generating a typical scenario of offshore wind farm output according to an embodiment of the present invention;
FIG. 2 is a graph of a light wind sky output scenario provided by an embodiment of the present invention;
FIG. 3 is a graph of a strong wind sky output scenario provided by an embodiment of the present invention;
FIG. 4 is a graph of a high wind power scenario provided by an embodiment of the present invention;
FIG. 5 is a graph of marine extreme weather output scenario provided by an embodiment of the present invention. .
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, an embodiment of the present invention provides a method for generating an output typical scenario of an offshore wind farm, including:
s101, acquiring, detecting and processing the output data of the offshore wind farm and the offshore meteorological data, and acquiring new output data of the offshore wind farm and offshore meteorological data.
Specifically, the output data of the offshore wind farm is recorded as P, the offshore meteorological data including the wind speed v, the atmospheric pressure P, the air temperature T, the precipitation R, the relative humidity U, the cloud quantity C and other climate elements of the offshore wind farm are collected, the collected output data of the offshore wind farm and the collected climate element data are sequences of corresponding data and time, the data need to be collected from a long enough time scale, and the time span is longer than one quarter. And detecting and processing the collected output data of the offshore wind farm and the collected offshore meteorological data, including correction of missing data, correction of abnormal data and screening of ocean extreme weather.
The missing data correction includes correcting the data by linear interpolation if the number of missing data does not exceed the error allowable limit.
The abnormal data correction comprises the steps of verifying data exceeding a data limit value or short-time mutation, deleting the data exceeding the data limit value or short-time mutation, and correcting missing data.
Marine extreme weather screening includes screening out marine wind farm output data in marine extreme weather and analyzing separately based on the influence of marine extreme weather on marine wind farm output.
The method is used for establishing a foundation for further modeling the output of the offshore wind farm under different ocean weather characteristics, detecting and processing the output data of the offshore wind farm and the offshore meteorological data of the selected scene, and improving the extraction precision.
S102. Dividing the new output data of the offshore wind farm and the new meteorological data into the S season according to the characteristics of the ocean monsoon, screening the new output data of the offshore wind farm and the new meteorological data, and obtaining k weather characteristic indexes of the S-th season output of the offshore wind farm
Wherein the weather characteristics include ocean non-extreme weather and ocean extreme weather.
Specifically, according to the characteristic of marine monsoon in the sea area of the offshore wind farm, the characteristic of the output of the offshore wind farm in each season is considered, and the output data of the offshore wind farm and the output data of the offshore meteorological data are divided into S seasons for analysis. In the multiple regression analysis of the output of the offshore wind farm and the climate elements, the Pearson correlation coefficient is used as a method for reflecting the correlation among the multiple variables, and when the nonlinear relation exists among the variables, linear transformation is needed. The factors affected by the wind farm output in different seasons S may be different, and the correlation coefficients of the seasons are calculated for the S seasons respectively.
Constructing a multiple regression analysis equation according to new offshore wind farm output data and climate data, wherein the Pearson correlation coefficient R of the s-th offshore wind farm output x and the climate data y xy The following are provided:
wherein,,
and->
Is the sample mean of the output data x and the climate factor data y. If the correlation coefficient R xy Is positive, is used for representing that the output data x of the offshore wind farm and the climate data y are positively correlated, if the correlation coefficient R xy Is negative, is used for representing that the output data x and the climate data y of the offshore wind farm are in negative correlation, wherein the absolute value of the correlation coefficient tends to be 1, and represents the offshore wind farmThe stronger the correlation between the output data x and the climate data y is, the absolute value of the correlation coefficient tends to be 0, which means that the weaker the correlation between the output data x and the climate data y of the offshore wind farm is, according to the correlation coefficient R xy >Weather data y of 0.4 are obtained, and weather characteristic index of the s-th season output of the offshore wind farm is obtained>
Based on offshore ocean quarterly weather with obvious influence on offshore wind power generation in offshore ocean areas, the output data and the meteorological data are studied in S seasons, weather characteristic indexes influencing the output of the offshore wind power plant are accurately identified through correlation analysis, a foundation is established for the output modeling of the offshore wind power plant under different ocean weather characteristics, and the accuracy and the representativeness of selected scenes are improved.
S1031, according to the characteristic index of the ocean non-extreme weather
And determining a typical scene of the offshore wind farm output and probability thereof in the s Ji Haiyang non-extreme weather.
Specifically, the characteristic indexes of the ocean non-extreme weather comprise the daily average output
Peak output of daily load
Peak daily load output->
Based on the s Ji Haiyang non-extreme weather offshore wind farm output curve P s Pressing the button
Sequencing from small to large, and setting daily load peak output ++under consideration of the condition of low output>
Confidence level alpha E (0, 1), and screening out that the peak output of the load on the s th season is not less than +.>
The probability of (a) is greater than alpha, the daily output curve of the offshore wind farm +.>
According to the daily output curve of the offshore wind farm
Screening out average output +.>
Not less than the daily output curve of the offshore wind farm +.>
Confidence level epsilon (0, 1) of the solar output curve P of the offshore wind farm ε s The following are provided:
considering influence of ocean weather on output of offshore wind farm, and using weather characteristic index for influencing output of offshore wind farm in the s th season
As a judging basis of similar weather, when the wind speed is used as a weather characteristic index, the daily average wind speed is used for dividing weather characteristics into breeze days, strong wind days and ocean extreme weather. Corresponding to various non-extreme ocean weather +.>
Considering the situation that the output of the offshore wind farm is insufficient and severe in the peak load period to reserve enough spare capacity in advance, P is calculated ε s Peak output of middle day>
A minimum sunrise force curve is used as a typical scene +.about.f of the output of the offshore wind farm under the s-th season confidence level alpha in the ocean extreme weather W>
According to the daily output curve P of the offshore wind farm ε s Determining typical output scene of offshore wind farm in ocean non-extreme weather W under s-th season confidence level alpha
The following are provided:
the probability of occurrence of the output typical scene can be approximately evaluated on the condition of the output of the offshore wind farm under the weather W and the confidence level alpha, the smaller the probability is, the more the offshore wind farm is affected by the weather, and the probability of the output typical scene of the offshore wind farm is calculated as follows:
wherein,,
for the probability of occurrence of the typical scenario of output under weather W and confidence level alpha, N ε P in weather W ε s Total number of medium day output curves.
According to the method, based on the marine quaternary climate with remarkable influence on offshore wind power generation in an offshore area, output data and meteorological data are studied in a divided S season, weather characteristic indexes influencing the output of the offshore wind power plant are accurately identified through correlation analysis, a foundation is established for the output modeling of the offshore wind power plant under the ocean non-extreme weather characteristics, and the accuracy and the representativeness of selected scenes are improved;
s1032, according to the characteristic index of the extreme sky
And determining a typical scene of the output of the offshore wind farm in the extreme weather of the s < th > season ocean and probability thereof.
In particular, the output of the offshore wind farm is close to full-scale in consideration of the extreme weather of high wind speed such as tropical storm and the like, and the average output is calculated by the daily
Based on the output curve P of the offshore wind farm in the extreme weather of the s th ocean s' Press->
Sequencing from small to large, wherein the characteristic indexes of ocean extreme weather comprise daily average output +.>
Peak daily load output->
Setting daily load peak output +.>
Confidence level beta E (0, 1), and screening out that the peak output of the load on the s th season is not less than +.>
Is greater than beta, the daily output curve of the offshore wind farm +.>
The following are provided:
weather characteristic index for influencing s-th output of offshore wind farm
As the judgment basis of the similar ocean extreme weather. Ocean weather corresponding to various extremes +.>
Will->
Average output of middle day->
The maximum sunrise force curve is taken as +.f. of a typical output scene of the offshore wind farm under the ocean extreme weather W' under the s-th season confidence level beta>
Typical scenario of output of offshore wind farm in ocean extreme weather W under s-th season confidence level beta
The following are provided:
the probability of occurrence of the typical output scene reflects the probability of occurrence of the condition that the average output of the offshore wind farm is highest daily under the ocean extreme weather under the weather W' and the confidence level beta, and the output condition of the offshore wind farm under the ocean extreme weather can be approximately evaluated. The probability of a typical scenario of the offshore wind farm output is calculated as follows:
wherein,,
is the occurrence probability of the typical output scene under the weather W' and the confidence level beta, N β For weather W->
Total number of medium day output curves.
According to the invention, based on the marine quaternary climate with remarkable influence on offshore wind power generation in offshore areas, the huge influence of ocean extreme weather on the output of the offshore wind power plant is fully considered, the output data and the meteorological data are divided into s seasons for research, and weather characteristic indexes influencing the output of the offshore wind power plant are accurately identified through correlation analysis, so that a foundation is established for the output modeling of the offshore wind power plant under different ocean weather characteristics, and the accuracy and the representativeness of the selected scene are improved.
In one embodiment, the output data and the meteorological data are divided into quarters, the weather characteristic indexes are determined through correlation analysis, then the output characteristic indexes of the offshore wind farm in ocean non-extreme weather and ocean extreme weather are calculated, and finally the output typical scene is screened according to confidence level setting. The analysis is performed on the basis of annual output data of 2019 of a large offshore wind farm in the southeast China, and the sampling time interval of the output data and the meteorological data is one hour.
(1) And dividing the output data of the offshore wind farm and the offshore meteorological data into s seasons according to the marine monsoon characteristics of the sea area at the offshore wind farm for analysis. Wherein s= {1,2,3,4}, respectively represent four quarters of 2019.
(2) And performing multiple regression analysis on four climate elements including output data P, wind speed v, temperature T, precipitation R and cloud quantity C of the offshore wind farm, reflecting the correlation between the output and one of the climate elements by using a Pearson correlation coefficient, and screening the climate elements with strong correlation as weather characteristic indexes in s seasons. The calculated correlation coefficients of the wind power plant output and four climate elements are shown in table 1, so that the wind speed v is used as a weather characteristic index for the offshore wind power plant, and the corresponding weather characteristics are ocean non-extreme weather and ocean extreme weather, wherein the ocean non-extreme weather in the weather characteristics is divided into three types of breeze, strong wind and strong wind in detail.
TABLE 1 correlation coefficients of wind farm output and four climate factors
| S | v | | R | C | |
| 1 | 0.497 | -0.135 | 0.073 | 0.135 | |
| 2 | 0.473 | -0.189 | 0.137 | -0.014 | |
| 3 | 0.693 | -0.044 | 0.148 | 0.206 | |
| 4 | 0.500 | -0.215 | -0.042 | 0.164 |
(3) And calculating the characteristic index of the daily output curve of the offshore wind farm in the s Ji Haiyang non-extreme weather, and calculating the characteristic index of the daily output curve of the offshore wind farm in the s-th ocean extreme weather.
(4) Referring to fig. 2,3,4 and 5, a set P of daily output curves of the offshore wind farm in s season under ocean non-extreme weather is screened by setting a confidence level α=0.9 and an average output index ε=0.2 based on daily peak output ε s Selecting a sunrise force curve with the smallest daily load peak output index as an output typical scene
(5) Based on the daily average output, setting confidence level gamma=0.95, screening a set of daily output curves of the offshore wind farm in s seasons under ocean extreme weather, and selecting a sunrise output curve with the maximum daily average output index as an output scene under the ocean extreme weather
The scenes in each season in the graph have high daily average output, reflect the condition that the offshore wind power is close to full-time under the ocean extreme weather, and can be used for considering the influence of the ocean extreme weather output on unit scheduling, system peak shaving and the like.
According to the invention, based on offshore wind power generation influence on offshore quaternary climate in offshore sea areas, the output data and the meteorological data are divided into s seasons for research, and weather characteristic indexes influencing the output of the offshore wind power plant are accurately identified through correlation analysis, a foundation is established for the output modeling of the offshore wind power plant under different ocean weather characteristics, the accuracy and the representativeness of the selected scene are improved, the invention also provides a conventional offshore wind power plant output representative scene selection method and a probability calculation method thereof, the scene can reflect the output condition of the offshore wind power plant under various ocean weather characteristics, the scene can be used for operation simulation and benefit evaluation of an offshore wind power system after the future high-proportion offshore wind power grid connection, the huge influence of ocean extreme weather on the output of the offshore wind power plant is fully considered, the selection method of the offshore wind power plant output representative scene under the extreme weather is creatively provided, the scene is applied to the system operation simulation, the safety and stability of a power grid can be further ensured, the offshore wind power plant scheme is optimized, and the offshore wind power consumption rate is improved.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the offshore wind farm output exemplary scenario generation method of any one of the embodiments described above. For example, the computer readable storage medium may be a memory including program instructions as described above, where the program instructions are executable by a processor of a computer terminal device to perform the method for generating a typical scenario of offshore wind farm output as described above, and achieve technical effects consistent with the method as described above.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (9)
1. The method for generating the typical scene of the output of the offshore wind farm is characterized by comprising the following steps of:
acquiring, detecting and processing offshore wind farm output data and offshore meteorological data to acquire new offshore wind farm output data and offshore meteorological data, comprising: the detection and processing comprises missing data correction, abnormal data correction and ocean extreme weather screening; the missing data correction comprises the step of correcting data by adopting linear interpolation if the number of missing data does not exceed the limit value allowed by errors; the abnormal data correction comprises the steps of verifying data exceeding a data limit value or short-time mutation, deleting the data exceeding the data limit value or short-time mutation and correcting missing data; the marine extreme weather screening comprises the steps of screening out the output data of the offshore wind farm under the marine extreme weather according to the influence of the marine extreme weather on the output of the offshore wind farm, and analyzing the output data independently;
dividing the new output data of the offshore wind farm and the new meteorological data into the S season according to the characteristics of the ocean monsoon, screening the new output data of the offshore wind farm and the new meteorological data, and obtaining k weather characteristic indexes of the S-th season output of the offshore wind farm
Wherein the weather characteristics include ocean non-extreme weather and ocean extreme weather;
according to the characteristic index of the ocean non-extreme weather, determining an offshore wind farm output typical scene and probability thereof under the s Ji Haiyang non-extreme weather;
and determining a typical output scene of the offshore wind farm in the s-th ocean extreme weather and probability of the typical output scene according to the characteristic index of the ocean extreme weather.
2. The method for generating a typical scenario of offshore wind farm output according to claim 1, wherein the new offshore wind farm output data and offshore meteorological data are divided into S seasons according to ocean monsoon characteristics, the new offshore wind farm output data and offshore meteorological data are screened, and k weather characteristic indexes of the S-th season output of the offshore wind farm are obtained
Wherein the weather features include ocean non-extreme weather and ocean extreme weather, including:
based on the new offshore wind farm output data andconstructing a multiple regression analysis equation by using climate data, and constructing a Pearson correlation coefficient R of the output x of the s-th offshore wind farm and the climate data y xy The following are provided:
wherein,,
and->
Is the sample mean of the output data x and the climate data y.
3. The method for generating a typical scenario of offshore wind farm output according to claim 2, wherein the new offshore wind farm output data and offshore meteorological data are divided into S seasons according to ocean monsoon characteristics, and the new offshore wind farm output data and offshore meteorological data are screened to obtain weather characteristic indexes of the S th season output of the offshore wind farm
Wherein the weather features include ocean non-extreme weather and ocean extreme weather, including:
if the correlation coefficient R xy Positive, the method is used for representing that the output data x of the offshore wind farm and the climate data y are positively correlated;
if the correlation coefficient R xy Negative, for representing that the output data x and the climate data y of the offshore wind farm are in negative correlation;
wherein the absolute value of the correlation coefficient tends to be 1, which indicates that the correlation between the offshore wind farm output data x and the climate data y is stronger, and the absolute value of the correlation coefficient tends to be 0, which indicates that the correlation between the offshore wind farm output data x and the climate data y is weaker.
4. According to claim 3The method for generating the typical scene of the output of the offshore wind farm is characterized in that the new output data of the offshore wind farm and the new weather data of the offshore wind farm are divided into S seasons according to the characteristics of ocean monsoon, the new output data of the offshore wind farm and the new weather data of the offshore wind farm are screened, and weather characteristic indexes of the S th season output of the offshore wind farm are obtained
Wherein the weather features include ocean non-extreme weather and ocean extreme weather, including:
according to the correlation coefficient R xy >Weather data y of 0.4 are obtained, and weather characteristic indexes of the s-th season output of the offshore wind farm are obtained
5. The method for generating the typical scenario of the output of the offshore wind farm according to claim 2, wherein the determining the typical scenario of the output of the offshore wind farm and the probability thereof in the s Ji Haiyang non-extreme weather according to the characteristic index of the marine non-extreme weather comprises:
the characteristic indexes of the ocean non-extreme weather comprise that the daily average output is
Peak daily load output->
Setting the daily peak load output
Confidence level alpha E (0, 1), and screening out that the peak output of the load on the s th season is not less than +.>
Is greater than alphaWind farm daily output curve +.>
The following are provided:
according to the daily output curve of the offshore wind farm
Screening out average output +.>
Not less than the daily output curve of the offshore wind farm +.>
Confidence level ε (0, 1) daily output curve of offshore wind farm ∈>
The following are provided:
6. the method for generating a typical scenario of the output of the offshore wind farm according to claim 5, wherein the determining the typical scenario of the output of the offshore wind farm and the probability thereof in the s Ji Haiyang non-extreme weather according to the characteristic index of the marine non-extreme weather comprises:
according to the daily output curve of the offshore wind farm
Determining the classical output of an offshore wind farm in the ocean extreme weather W at the s-th level of confidence alphaScene->
The following are provided:
the probability of the typical scene of the output of the offshore wind farm is calculated as follows:
wherein,,
for the probability of occurrence of the typical scenario of output under weather W and confidence level alpha, N ε For weather W->
Total number of medium day output curves.
7. The method for generating the typical scenario of the output of the offshore wind farm according to claim 2, wherein the determining the typical scenario of the output of the offshore wind farm in the s-th ocean extreme weather and the probability thereof according to the characteristic index of the ocean extreme weather comprises:
the characteristic indexes of the ocean extreme weather comprise that the average output is daily
Peak daily load output->
Setting the daily peak load output
Confidence level beta E (0, 1), and screening out that the peak output of the load on the s th season is not less than +.>
Is greater than beta, the daily output curve of the offshore wind farm +.>
The following are provided:
8. a method of generating a typical scenario of offshore wind farm output according to claim 3, wherein determining the typical scenario of offshore wind farm output and the probability thereof in the s-th ocean extreme weather according to the characteristic index of the ocean extreme weather comprises:
typical scenario of output of offshore wind farm in ocean extreme weather W' under s-th season confidence level beta
The following are provided:
the probability of the typical scene of the output of the offshore wind farm is calculated as follows:
wherein,,
is typical of the output at weather W' and confidence level betaProbability of occurrence of scene, N β For weather W->
Total number of medium day output curves.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of generating a marine wind farm output profile as claimed in any of claims 1 to 8.
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